OBD SecureAlert: An Anomaly Detection System for Vehicles

Abstract: Vehicles can be considered as a specialized form
of Cyber Physical Systems with sensors, ECU’s and actuators
working together to produce a coherent behavior. With the advent
of external connectivity, a larger attack surface has opened up
which not only affects the passengers inside vehicles, but also
people around them. One of the main causes of this increased
attack surface is because of the advanced systems built on top of
old and less secure common bus frameworks which lacks basic
authentication mechanisms. To make such systems more secure,
we approach this issue as a data analytic problem that can detect
anomalous states. To accomplish that we collected data flowing
between different components from real vehicles and using a
Hidden Markov Model, we detect malicious behaviors and issue
alerts, while a vehicle is in operation. Our evaluations using
single parameter and two parameters together provide enough
evidence that such techniques could be successfully used to detect
anomalies in vehicles. Moreover our method could be used in new
vehicles as well as older ones.